Despite all of their potential, artificial intelligence agents have been slow to impact business, and a new startup is betting that the reason they haven’t is a lack of context.
Launched as part of Y Combinator’s Summer 2025 cohort, Trace is a workflow orchestration startup that aims to fill this gap. The company maps complex enterprise environments and processes so agents have the context they need to scale quickly.
“OpenAI and Anthropic are building these brilliant practitioners that can be leveraged within the company,” says Trace CEO Tim Cherkasov, referring to AI Labs’ tools. “We are building the manager who knows where to put things.”
On Thursday, the London-based company said it had raised $3 million in seed funding from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital and WeFunder. Angel investors Benjamin Bryant and Kevin Moore also invested.
Trace’s system begins by creating a knowledge graph from a company’s existing tools—systems like email, Slack, and Airtable that shape the company’s daily work life. With this framework, users can ask the system for a high-level task—like “We need to design a new microsite” or “Let’s develop our sales plan for 2027″—and Trace will return with a step-by-step workflow, delegating some tasks to AI agents and delegating others to human workers. When the system invokes an AI agent, it will ask it for the specific data needed to complete its secondary task.
The idea is to automate the delicate work of onboarding AI agents, one of the biggest blocks to real growth within companies.
With so many companies focusing on AI, Trace will have plenty of competition. Earlier this week, Anthropic released its own take on business agents, focused on pre-built plugins for specific department functions. And many of the workplace productivity services that Trace will draw on, such as Atlassian’s Jira, are launching their own agents, potentially competing with the startup’s system.
Techcrunch event
Boston, MA
|
June 9, 2026
But Trace’s founders believe their knowledge graph approach will be the key to success, as they can embed context engineering deep into the fabric of agent development.
“2024 and 2025 were still about direct engineering. Now we’ve moved from direct engineering to ambient engineering,” says CTO Artur Romanov. “Whoever provides the best framework at the right time will be the infrastructure on top of which the first AI companies will be built. And we hope it will be that infrastructure.”
